Polarization-based non-linear deep diffractive neural networks

Coherent light-based optical computing faces challenges in implementing low-power optical nonlinearities, which are essential for optimal neural network performance. This limitation restricts the overall performance and applicability of these networks. To address this, we propose a polarization encoding scheme that enables a low-power nonlinearity. By projecting polarization to amplitude in a sinusoidal manner and subsequently converting amplitude back to the polarization domain, we can implement a non-linear diffractive neural network. The neural network utilizes polarization-encoded inputs and polarization rotation-encoded weights. Optical interconnects between neurons are achieved through the diffraction of the spatially inhomogeneously polarized wavefront. Our approach offers a low-power solution for incorporating nonlinearity into deep diffractive neural networks.